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The Forecasting Performance of Dynamic Factor Models with Vintage Data

Author

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  • Luca Di Bonaventura
  • Mario Forni
  • Francesco Pattarin
Abstract
We present a comparative analysis of the forecasting performance of two dynamic factor models, the Stock and Watson (2002a, b) model and the Forni, Hallin, Lippi and Reichlin (2005) model, based on vintage data. Our dataset that contains 107 monthly US “first release” macroeconomic and financial vintage time series, spanning the 1996:12 to 2017:6 period with monthly periodicity, extracted from the Bloomberg database. We compute real-time one-month-ahead forecasts with both models for four key macroeconomic variables: the month-on-month change in industrial production, the unemployment rate, the core consumer price index and the ISM Purchasing Managers’ Index. First, we find that both the Stock and Watson and the Forni, Hallin, Lippi and Reichlin models outperform simple autoregressions for industrial production, unemployment rate and consumer prices, but that only the first model does so for the PMI. Second, we find that neither models always outperform the other. While Forni, Hallin, Lippi and Reichlin’s beats Stock and Watson’s in forecasting industrial production and consumer prices, the opposite happens for the unemployment rate and the PMI.

Suggested Citation

  • Luca Di Bonaventura & Mario Forni & Francesco Pattarin, 2018. "The Forecasting Performance of Dynamic Factor Models with Vintage Data," Center for Economic Research (RECent) 138, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    • Handle: RePEc:mod:recent:138
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    References listed on IDEAS

    as
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    Cited by:

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    4. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2021. "Time-varying general dynamic factor models and the measurement of financial connectedness," Journal of Econometrics, Elsevier, vol. 222(1), pages 324-343.
    5. Barigozzi, Matteo & Hallin, Marc & Luciani, Matteo & Zaffaroni, Paolo, 2024. "Inferential theory for generalized dynamic factor models," Journal of Econometrics, Elsevier, vol. 239(2).
    6. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.
    7. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
    8. Chiara Pederzoli & Costanza Torricelli, 2019. "The impact of the Fundamental Review of the Trading Book: A preliminary assessment on a stylized portfolio," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0075, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    9. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    10. Marc Hallin, 2022. "Manfred Deistler and the General Dynamic Factor Model Approach to the Analysis of High-Dimensional Time Series," Working Papers ECARES 2022-30, ULB -- Universite Libre de Bruxelles.
    11. Marianna Brunetti & Roberta De Luca, 2023. "Pre-selection in cointegration-based pairs trading," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1611-1640, December.
    12. Francesca Arnaboldi, Francesca Gioia, 2019. "Portfolio choice: Evidence from new-borns," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0078, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    13. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2019. "Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness," Working Papers ECARES 2019-09, ULB -- Universite Libre de Bruxelles.
    14. Barbara Rossi, 2019. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," Working Papers 1162, Barcelona School of Economics.
    15. Fan Yang & Robert C. Qiu & Zenan Ling & Xing He & Haosen Yang, 2019. "Detection and Analysis of Multiple Events Based on High-Dimensional Factor Models in Power Grid," Energies, MDPI, vol. 12(7), pages 1-16, April.
    16. Siegfried Hörmann & Gilles Nisol, 2021. "Prediction of Singular VARs and an Application to Generalized Dynamic Factor Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 295-313, May.
    17. Costanza Torricelli & Fabio Ferrari, 2022. "Climate Stress Test: bad (or good) news for the market? An Event Study Analysis on Euro Zone Banks," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0086, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    18. Costanza Torricelli & Beatrice Bertelli, 2022. "ESG compliant optimal portfolios: The impact of ESG constraints on portfolio optimization in a sample of European stocks," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0088, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".

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    More about this item

    Keywords

    Dynamic factor models; Forecasting; Forecasting Performance; Vintage data; First release data;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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